# coding=utf-8 # Copyright 2026 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import gc import random import unittest import numpy as np import torch from PIL import Image from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, AutoPipelineForInpainting, PNDMScheduler, StableDiffusionPAGInpaintPipeline, UNet2DConditionModel, ) from ...testing_utils import ( backend_empty_cache, enable_full_determinism, floats_tensor, load_image, require_torch_accelerator, slow, torch_device, ) from ..pipeline_params import ( TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, ) from ..test_pipelines_common import ( IPAdapterTesterMixin, PipelineFromPipeTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, ) enable_full_determinism() class StableDiffusionPAGInpaintPipelineFastTests( PipelineTesterMixin, IPAdapterTesterMixin, PipelineLatentTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase, ): pipeline_class = StableDiffusionPAGInpaintPipeline params = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS.union({"pag_scale", "pag_adaptive_scale"}) batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS image_params = frozenset([]) image_latents_params = frozenset([]) callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union( {"add_text_embeds", "add_time_ids", "mask", "masked_image_latents"} ) def get_dummy_components(self, time_cond_proj_dim=None): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(32, 64), time_cond_proj_dim=time_cond_proj_dim, layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, ) scheduler = PNDMScheduler(skip_prk_steps=True) torch.manual_seed(0) vae = AutoencoderKL( block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=4, ) torch.manual_seed(0) text_encoder_config = CLIPTextConfig( bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1000, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") components = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, "image_encoder": None, } return components def get_dummy_inputs(self, device, seed=0): # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device) image = image.cpu().permute(0, 2, 3, 1)[0] init_image = Image.fromarray(np.uint8(image)).convert("RGB").resize((64, 64)) # create mask image[8:, 8:, :] = 255 mask_image = Image.fromarray(np.uint8(image)).convert("L").resize((64, 64)) if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) inputs = { "prompt": "A painting of a squirrel eating a burger", "image": init_image, "mask_image": mask_image, "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "strength": 1.0, "pag_scale": 0.9, "output_type": "np", } return inputs def test_pag_applied_layers(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() # base pipeline pipe = self.pipeline_class(**components) pipe = pipe.to(device) pipe.set_progress_bar_config(disable=None) # pag_applied_layers = ["mid","up","down"] should apply to all self-attention layers all_self_attn_layers = [k for k in pipe.unet.attn_processors.keys() if "attn1" in k] original_attn_procs = pipe.unet.attn_processors pag_layers = [ "down", "mid", "up", ] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(all_self_attn_layers) # pag_applied_layers = ["mid"], or ["mid.block_0"] or ["mid.block_0.attentions_0"] should apply to all self-attention layers in mid_block, i.e. # mid_block.attentions.0.transformer_blocks.0.attn1.processor # mid_block.attentions.0.transformer_blocks.1.attn1.processor all_self_attn_mid_layers = [ "mid_block.attentions.0.transformer_blocks.0.attn1.processor", # "mid_block.attentions.0.transformer_blocks.1.attn1.processor", ] pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["mid"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers) pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["mid_block"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers) pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["mid_block.attentions.0"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers) # pag_applied_layers = ["mid.block_0.attentions_1"] does not exist in the model pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["mid_block.attentions.1"] with self.assertRaises(ValueError): pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) # pag_applied_layers = "down" should apply to all self-attention layers in down_blocks # down_blocks.1.attentions.0.transformer_blocks.0.attn1.processor # down_blocks.1.attentions.0.transformer_blocks.1.attn1.processor # down_blocks.1.attentions.0.transformer_blocks.0.attn1.processor pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["down"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert len(pipe.pag_attn_processors) == 2 pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["down_blocks.0"] with self.assertRaises(ValueError): pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["down_blocks.1"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert len(pipe.pag_attn_processors) == 2 pipe.unet.set_attn_processor(original_attn_procs.copy()) pag_layers = ["down_blocks.1.attentions.1"] pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False) assert len(pipe.pag_attn_processors) == 1 def test_pag_inference(self): device = "cpu" # ensure determinism for the device-dependent torch.Generator components = self.get_dummy_components() pipe_pag = self.pipeline_class(**components, pag_applied_layers=["mid", "up", "down"]) pipe_pag = pipe_pag.to(device) pipe_pag.set_progress_bar_config(disable=None) inputs = self.get_dummy_inputs(device) image = pipe_pag(**inputs).images image_slice = image[0, -3:, -3:, -1] assert image.shape == ( 1, 64, 64, 3, ), f"the shape of the output image should be (1, 64, 64, 3) but got {image.shape}" expected_slice = np.array([0.7173, 0.5821, 0.6031, 0.5765, 0.6412, 0.6558, 0.5803, 0.5675, 0.5246]) max_diff = np.abs(image_slice.flatten() - expected_slice).max() assert max_diff < 1e-3, f"output is different from expected, {image_slice.flatten()}" def test_encode_prompt_works_in_isolation(self): extra_required_param_value_dict = { "device": torch.device(torch_device).type, "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0, } return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict, atol=1e-3, rtol=1e-3) @slow @require_torch_accelerator class StableDiffusionPAGPipelineIntegrationTests(unittest.TestCase): pipeline_class = StableDiffusionPAGInpaintPipeline repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5" def setUp(self): super().setUp() gc.collect() backend_empty_cache(torch_device) def tearDown(self): super().tearDown() gc.collect() backend_empty_cache(torch_device) def get_inputs(self, device, generator_device="cpu", seed=0, guidance_scale=7.0): img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" init_image = load_image(img_url).convert("RGB") mask_image = load_image(mask_url).convert("RGB") generator = torch.Generator(device=generator_device).manual_seed(seed) inputs = { "prompt": "A majestic tiger sitting on a bench", "generator": generator, "image": init_image, "mask_image": mask_image, "strength": 0.8, "num_inference_steps": 3, "guidance_scale": guidance_scale, "pag_scale": 3.0, "output_type": "np", } return inputs def test_pag_cfg(self): pipeline = AutoPipelineForInpainting.from_pretrained(self.repo_id, enable_pag=True, torch_dtype=torch.float16) pipeline.enable_model_cpu_offload(device=torch_device) pipeline.set_progress_bar_config(disable=None) inputs = self.get_inputs(torch_device) image = pipeline(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array( [0.38793945, 0.4111328, 0.47924805, 0.39208984, 0.4165039, 0.41674805, 0.37060547, 0.36791992, 0.40625] ) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3, ( f"output is different from expected, {image_slice.flatten()}" ) def test_pag_uncond(self): pipeline = AutoPipelineForInpainting.from_pretrained(self.repo_id, enable_pag=True, torch_dtype=torch.float16) pipeline.enable_model_cpu_offload(device=torch_device) pipeline.set_progress_bar_config(disable=None) inputs = self.get_inputs(torch_device, guidance_scale=0.0) image = pipeline(**inputs).images image_slice = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 512, 3) expected_slice = np.array( [0.3876953, 0.40356445, 0.4934082, 0.39697266, 0.41674805, 0.41015625, 0.375, 0.36914062, 0.40649414] ) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3, ( f"output is different from expected, {image_slice.flatten()}" )